One Year of Visualizing the Pandemic

This week, one year ago, COVID-19 hit the U.S. with full force.

In early March of last year, as our team searched for reliable data sources to start collecting and analyzing spatial patterns associated with COVID’s spread, we quickly realized that the tool and the data we needed to do this simply did not exist yet. There was a gap to fill. Less than two weeks later, with the hard work and dedication of our team and research coalition volunteers, the US COVID Atlas was born, built and publicly launched. We were one of the first open-source, near-real time data visualization tools integrating multiple county-level COVID data sources.

Fast forward to today, COVID-19 has caused the deaths of more than 500,000, infected millions more, shut down businesses, crippled local economies, and changed life irrevocably. From its initial launch, the Atlas has grown and evolved alongside the needs of the healthcare community, business owners, local policy makers, and the general public. But what did not change was the need for an open source tool that made COVID data accessible and the most impacted counties and communities visible. We also quickly realized that COVID data needed to be paired with community health and other contextual factors to capture the true multi-faceted impact of the pandemic.

After a year of tracking pandemic data, spatial statistical trends, and community health impacts, we’ve collected our top insights — and reflected on where we go from here.

Open source data and open science tools are critical to future planning and mitigation efforts.

This won’t be our last pandemic. We need data tools that can be quickly built, shared, and replicated to support collaborative and comprehensive research. Open source platforms can help deliver information to the general public quickly and can be adapted or improved as more information becomes available. For the Atlas, we integrated a participatory design for web application & infrastructure development from the beginning. Today, we have an iterative process of exploration, design, and prototyping that engages research coalition partners, contributors, and volunteers to constantly improve and refine our methods.

Highlighting areas with legacy racist and colonialist policies can help reveal the disproportionate toll of the pandemic on communities of color.

We must acknowledge the impact of systemically racist policies of the past and present in our analyses of disparate COVID outcomes. Spatial inequalities continue to plague our country. Geographic overlays showcasing disproportionately hit communities, such as hypersegregated cities and Native American tribal areas, call critical attention to counties and regions with persistently high COVID positivity, hospitalization and death rates. These insights are critical for tracking an equitable recovery and vaccination plan.

We need quickly accessible, multi-dimensional data communication tools.

No single data point or statistic can capture the entirety of COVID-19. Uncertainty should be incorporated by design into future communication and research models. To assess and predict varying levels of risk across diverse communities, data tools should incorporate multi-variate views across health, social economic, and demographic outcomes to support the real-time analysis of a complex, nuanced pandemic landscape.

Mobility data may provide additional valuable insight into understanding why and how the virus spread.

What can we learn from data showing where people were (or were not) working outside their homes? The Atlas’ new county-level mobility data via Safegraph shows where people have been staying at home compared to working full-time and part-time out at a workplace. Many factors may influence decisions to go out: economic and employment opportunities (essential worker jobs vs. remote work), stay-at-home orders, mask policies, school re-openings, behavioral trends, workplace demands, and office cultures, just to name a few. This is a piece of the story that hasn’t been explored in full yet — but deserves further analysis.

We must invest in local data collection and reporting systems.

At the start of the pandemic, data collection and public data availability were inconsistent and, in many places, not available at the local level. Identifying outbreaks early on is critical to isolating them effectively. By focusing on state-wide or even national metrics, it is easy to miss rapidly surging local cases and spillover across state boundaries. A year out and we have largely caught up with county-level reporting, but we are still missing variation at the census tract, zip code, and neighborhood levels. Local agencies need coordination, planning, and investment in their data infrastructure to ensure consistent and reliable information sharing.

There is no one data point that will capture the complexity of this pandemic. But we can better integrate both quantitative and qualitative data to add a much-needed human perspective. The US COVID Atlas will continue to grow and evolve in the next stage of this pandemic era and develop new features to support an equitable recovery.